MIT study finds that 14% of trials see approvals—higher than first thought

Big data analytics
Approval rates have been trending upward since 2013. (Image: iStockPhoto)

A 14% hit rate for drug approvals coming out of clinical trials might not sound great, but researchers at the Massachusetts Institute of Technology say this is much higher than they thought, and that approvals are bouncing up again after a period of decline.

The study, published in the journal Biostatistics this week, found that nearly 14% of all drugs in clinical trials gain a green light from the FDA, a much higher rate than previous studies had shown.

And drilling down into specific areas, the researchers found that approval rates for specific illnesses range from a high of 33.4% for infectious-disease vaccines, with cancer being the worst, with a low of 3.4%—not surprising, given the difficult nature of the disease. The study looked at the period 2000 to 2015.

There is a bright spot here: In 2015, this estimate more than doubled to 8.3%, “no doubt partly due to the recent progress in immuno-oncology,” the scientists say.

The research team also said that, while approval rates did fall between 2005 and 2013, they “have been trending upward since then.”

“One of the main responsibilities of investors and pharma executives is risk management, hence they need to know what the chances are that a compound will transition from phase 1 to phase 2 to phase 3 and, ultimately, receive FDA approval,” says Andrew W. Lo, the study’s senior author and director of MIT’s Laboratory for Financial Engineering.

“Without accurate and timely estimates, resources may be misallocated and financial returns may be misjudged, which leads to higher development costs, higher-priced drugs, and lost opportunities for investors and, more importantly, patients.”

The results of the research, which boasts itself as the largest study of its kind to date, came out of the Citeline dataset given out by U.K. company Informa.

These data were made up of around 400,000 entries corresponding to 185,994 unique clinical trials of over 21,000 compounds, with Lo and his colleagues devising an automated algorithm to trace each drug development path, infer phase transitions, and compute the probability of success (a.k.a. POS) stats.

Had they tried to do this with, it would have taken “months to years if the matching and inferences had to be done manually”, the researchers say. With their method, it was just hours.

The paper says that previous estimates of drug development success rates “rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases.” This new method, they say, should give a more accurate picture.

“You can’t manage what you don’t measure,” says Lo. “It would be literally impossible for any human to process all of these numbers by hand. But in the Era of Big Data, we can do this quickly and accurately.”

The researchers also found that studies using biomarkers “tend to be more successful” than those which don’t use them, something the study suggests as pointing to the “growing role of companion diagnostics in improving clinical trial success rates.”

According to Lo, the study is part of an ongoing effort to help make the biomedical ecosystem more efficient. “We hope to provide this information on a regular basis—it's not just a one-shot deal,” he says.

“As clinical trial success rates improve for certain diseases, it’s likely that more investment capital will flow into those areas. For diseases where success rates stall, public policy can play an important role by increasing research funding or providing more incentives to risk-tolerant investors and philanthropic organizations.

“It’s kind of like the difference between driving with GPS today versus driving 20 years ago when maps and friends were the only navigational tools at our disposal,” Lo observes. “Our goal is to show all stakeholders the lay of the land so that they can make more informed decisions about where and how to direct their resources.”